R/Medicine

About

The goal of the R/Medicine conference is to encourage the adoption of statistical modeling and reproducible data processing in clinical practice by promoting the use of the R programming environment and ecosystem. 

R is a free and open-source programming language for reproducible statistical computing, data visualization, and application development. R is the gold standard in reproducible research in academia and industry and has powerful capabilities to create highly-customizable interactive analytic dashboards, as well as predictive models that employ machine learning, deep learning, and artificial intelligence. 

R/Medicine was formed through a collaborative effort of the R Consortium with academic and industry partners including Yale University, Stanford University, the Mayo Clinic, and RStudio, Inc (now RStudio, PBC). Presentations at R/Medicine conferences showcase how the R ecosystem is currently leveraged in medical applications including clinical trial design and analysis, personalized medicine, the development of machine learning models using  laboratory and patient record data, and reproducible research. 

The inaugural R/Medicine conference was a 2-day event held in 2018 at Yale University that featured distinguished speakers including Rob Tibshirani (Professor of Biomedical Data Science at Stanford and inventor of the Lasso method of regularized linear regression) and Max Kuhn (former Senior Director of Statistics at Pfizer and author of multiple textbooks on machine learning and the widely used “caret” package), and 150 attendees. For the second year, a half- day pre-conference workshop was added to the schedule that featured parallel workshops for attendees in order to provide data literacy and data science education.  

The 2020 R/Medicine Conference will be held in Philadelphia from August 27 through August 29th. It will include two tracks of pre-conference educational workshops: one foundational workshop, covering reproducible data analysis with a focus on health care professionals who have no programming background and a second workshop on practical machine learning with a focus on predicting outcomes from clinical data sets. The pre-conference workshops  will be followed by two days of presentations from representatives of biostatistics, the pharmaceutical industry, regulators, and the clinical laboratories. We aim to increase attendance from 150 to 240 attendees by expanding our marketing and awareness efforts along with forging new beneficial partnerships, especially with organizations in the clinical laboratory space.